import os
#
id_str = '06101017_2'

batch_size = 16

# predict_batch_size = 256

# train_batch_nums = [10000]

train_epochs = [20, 20]

lrs = [0.0003, 0.0001]

eval_internal = 1
eval_print_batch_interval = 20
train_print_batch_interval = 20

persistent_internal = 40

train_k_prob = 0.5

os.environ["CUDA_VISIBLE_DEVICES"] = '3'

gpu_allow_growth = True

feature_size = 400

cnn1_kernel = [1, 12, 1, 6]
cnn2_kernel = [1, 8, 6, 10]

rnn_hidden_size = 128

attention_hidden_size = 10

fc1_b = 64

emos = ['neu', 'ang', 'hap', 'sad']

optimizer_type = 'adam'

persist_checkpoint_file = './output/p-model/my-model-' + id_str

gt_npy = './result/gt_' + id_str + '.npy'
pr_npy = './result/pr_' + id_str + '.npy'
sids_npy = './result/sids_' + id_str + '.npy'
ts_npy = './result/ts_' + id_str + '.npy'

# save alpha for train
alpha_gt_npy = './result/alpha_gt_' + id_str + '.npy'
alpha_pr_npy = './result/alpha_pr_' + id_str + '.npy'
alpha_npy = './result/alpha_' + id_str + '.npy'
alpha_seq_len_npy = './result/alpha_seq_len_' + id_str + '.npy'

# save alpha for test
a_npy = './result/a_' + id_str + '.npy'

persist_bestacc_file = './output/bestacc-model/my-model-' + id_str
persist_bestloss_file = './output/bestloss-model/my-model-' + id_str

is_train = True

is_restore = False

# is_attention = True

is_shuffle_train = False

attention_type = None
# attention_type = 'cnn'
# attention_type = 'simple'

restore_file = './output/bestacc-model/my-model-06081324_atten3'
restart_epoch_i = 0

out_put_log = id_str+'.log'

tf_log_dir = './output/tf_log/'

# Data configuration
# data_dir = '/Users/d/Project/emotions/data/Spectrogram_EN_Var'
data_dir = '/home/ddy/projects/emotions/data/Spectrogram_EN_Var'

classes = ['neu', 'ang', 'hap', 'sad']
sess = ['Ses01', 'Ses02', 'Ses03', 'Ses04', 'Ses05']
# consider_sent_types = ['impro']
consider_sent_types = ['impro', 'script']
vali_test_ses = 2
vali_type = 'M'
test_type = 'F'

is_post_calc_len = False
